Dive into Deep Learning
Dive into Deep Learning
Interactive deep learning book with code, math, and discussions
Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow
Adopted at 500 universities from 70 countries
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- [Feb 2023] The book is forthcoming on Cambridge University Press (order). The Chinese version is the best seller at the largest Chinese online bookstore. Follow D2L's open-source project for the latest updates.
- [Dec 2022] JAX implementation is available! New topics of reinforcement learning, Gaussian processes, and hyperparameter optimization are added!
- [Jul 2022] Check out our new API for implementation and new topics like generalization in classification and deep learning, ResNeXt, CNN design space, and transformers for vision and large-scale pretraining.
- [May 2022] Join us to improve ongoing translations in Portuguese, Turkish, Vietnamese, Korean, and Japanese.
- [Dec 2021] We added a new option to run this book for free: check out SageMaker Studio Lab.
- [May 2021] Slides, Jupyter notebooks, assignments, and videos of the Berkeley course can be found at the syllabus page.
Each section is an executable Jupyter notebook
You can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning.
Mathematics + Figures + Code
We offer an interactive learning experience with mathematics, figures, code, text, and discussions, where concepts and techniques are illustrated and implemented with experiments on real data sets.
Active community support
You can discuss and learn with thousands of peers in the community through the link provided in each section.